Effective Instructional Strategies for the Development of Computational Thinking in Primary Education: A Systematic Literature Review

This study consists of a 17-year (2006-2021) systematic literature review on the effective instructional strategies for developing Computational Thinking (CT) in primary school students (K-5). The aim of this paper is to identify instructional strategies that have been implemented and evaluated by means of a pre- and post-test, with the aim of developing CT as a thinking process (i.e. going beyond the mere acquisition of CS concepts). The analysis considers the overall pedagogical approach, with the intention of overcoming both the archetypal approaches to coding and the assessment tools used to measure CT acquisition. The results show that there is a strong difference between K-3 and 4-5 classes, the former being more the context for educational robotics or unplugged activities; they also show that a task-based approach is prevalent, whose effectiveness is limited to the first access to CS activities, namely improving self-efficacy and reducing learning anxiety. Nevertheless, this study also identifies some interesting models of problem solving (such as IGGIA or CPS) that go beyond the puzzle-based approach, as well as two significant teaching strategies (IDC and DBL) that try to promote more meaningful learning (typically including elements of constructionism), while enabling curricular learning objectives.